Iyengar Yoga for Treating Symptoms of Osteoarthritis of the Knees: A Pilot Study
Bibliographic record
Abstract
OBJECTIVES: The American College of Rheumatology (ACR) Guidelines for the medical management of osteoarthritis (OA) emphasize the use of nonpharmacologic interventions including exercise. Implementation of an exercise program can be difficult for patients, and little is known about the benefits of alternative therapies such as yoga. The aim of this pilot study was to assess the feasibility of using yoga in the tradition of B.K.S. Iyengar to treat the symptoms of osteoarthritis of the knee. DESIGN: Participants were instructed in modified Iyengar yoga postures during 90-minute classes once weekly for 8 weeks. SUBJECTS: Participants met ACR criteria for osteoarthritis of the knee and completed a medical history and physical examination, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Arthritis Impact Measurement Scale 2 (AIMS2), Patient Global Assessment (GA) by Visual Analog Scale (VAS), Physician GA by VAS, and 50-foot Walk Time before and following an 8-week course of yoga instruction. Eleven (11) subjects enrolled, nine completed at least one session and seven (six of whom were obese) had data from pre- and post-course time points available for analysis. RESULTS: Statistically significant reductions in WOMAC Pain, WOMAC Physical Function, and AIMS2 Affect were observed when participants' status were compared to their pre-course status. WOMAC Stiffness, AIMS2 Symptoms, Social and Role, Physician GA, and Patient GA measured trends in improvement of symptoms. No adverse events from treatment were reported. CONCLUSIONS: This pilot study suggests that yoga may provide a feasible treatment option for previously yoga-naive, obese patients >50 years of age and offers potential reductions in pain and disability caused by knee OA. Future studies should compare yoga to other nonpharmacologic interventions for knee OA, such as patient education or quadriceps-strengthening exercises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".